# -*- coding: utf-8 -*-
from datetime import timedelta
# 引入依赖
from jms.dm.dm_customer_base_detail_dt import jms_dm__dm_customer_base_detail_dt
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator


jms_dm__dm_customer_changes_week_dt = SparkSqlOperator(
    task_id='jms_dm__dm_customer_changes_week_dt',
    email=['houwenlong@jtexpress.com','yl_bigdata@yl-scm.com'],
    depends_on_past=True,
    pool_slots=3,
    master='yarn',
    name='jms_dm__dm_customer_changes_week_dt_{{ execution_date | cst_ds }}',
    sql='jms/dm/dm_customer_changes_week_dt/execute.hql',
    driver_memory='4G',
    executor_memory='12G',
    executor_cores=3,
    num_executors=20,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    yarn_queue='pro',
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
          'spark.dynamicAllocation.maxExecutors': 30,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 60,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead': '2G',  # 堆外内存
          },
    hiveconf={'hive.exec.dynamic.partition': 'true',  # 开启动态分区
              'hive.exec.dynamic.partition.mode': 'nonstrict',  # 动态分区模式非严格
              },
    execution_timeout=timedelta(minutes=60),
)

jms_dm__dm_customer_changes_week_dt << [jms_dm__dm_customer_base_detail_dt
                                                 ]
